Various global or local communication networks (the Internet, the World Wide Web, local area networks and the like) offer a user a vast amount of information. The information includes a multitude of contextual topics, such as but not limited to, news and current affairs, maps, company information, financial information and resources, traffic information, games and entertainment related information. Users use a variety of client devices (desktop, laptop, notebook, smartphone, tablets and the like) to have access to rich content (like images, audio, video, animation, and other multimedia content from such networks).
The volume of available information through various Internet resources has grown exponentially in the past couple of years. Several solutions have been developed in order to allow a typical user to find the information that the user is looking for. One example of such a solution is a search engine. Examples of the search engines include GOOGLE™ search engine, YANDEX™ search engine, YAHOO!™ search engine and the like. The user can access the search engine interface and submit a search query associated with the information that the user is desirous of locating on the Internet. In response to the search query, the search engine provides a ranked list of search results. The ranked list of search results is generated based on various ranking algorithms employed by the particular search engine that is being used by the user performing the search. The overall goal of such ranking algorithms is to present the most relevant search results at the top of the ranked list, while less relevant search results would be positioned on less prominent positions of the ranked list of search results (with the least relevant search results being located towards the bottom of the ranked list of search results).
The search engines typically provide a good search tool for a search query that the user knows apriori that she/he wants to search. In other words, if the user is interested in obtaining information about the most popular destinations in Italy (i.e. a known search topic), the user could submit a search query: “The most popular destinations in Spain?” The search engine will then present a ranked list of Internet resources that are potentially relevant to the search query. The user can then browse the ranked list of search results in order to obtain information she/he is interested in as it related to places to visit in Spain. If the user, for whatever reason, is not satisfied with the uncovered search results, the user can re-run the search, for example, with a more focused search query, such as “The most popular destinations in Spain in the summer?”, “The most popular destinations in the South of Spain?”, “The most popular destinations for a culinary getaway in Spain?”.
There is another approach that has been proposed for allowing the user to discover content and, more precisely, to allow for discovering and/or recommending content that the user may not be expressly interested in searching for. In a sense, such systems recommend content to the user without an express search request based on explicit or implicit interests of the user.
An example of such a system is a FLIPBOARD™ recommendation system, which system aggregates and recommends content from various social networks. The FLIPBOARD recommendation system presents the uncovered content in a “magazine style” format, where the user can “flip” through the pages with the recommended/aggregated content. The recommendation system collects content from social media and other websites, presents it in magazine format, and allows users to “flip” through their social-networking feeds and feeds from websites that have partnered with the company, effectively “recommending” content to the user even though the user may not have expressly expressed her/his desire in the particular content.
Another example of the recommendation system is YANDEX.ZEN™ recommendation system. The Yandex.Zen recommendation system recommends digital content (such as articles, news, and video in a personalized feed on the Yandex.Browser start screen). As the user browses the Yandex.Zen server recommended content, the server acquires explicit (by asking whether the user likes to see more of such content in the user's feed) or implicit (by observing user content interactions) feedback. Using the user feedback, the Yandex.Zen server continuously improves the content recommendations presented to the given user.
US 2012/0209907 published on Aug. 16, 2012 to Andrews et al discloses a content aggregation and distribution service, which can execute in a cloud computing environment, provides content based on a broadcast user's topics of interest to a subscriber user based on the context of the subscriber. An example of a broadcast user is a celebrity. Content is automatically gathered about the broadcast user's designated topics of interest from online resources, and filtered and distributed based on a context of the subscriber. Some examples of online resources are websites, social networking sites, and purchase transaction systems. An example of broadcast content is a recommendation which may have been entered directly to the service or posted by the celebrity in his or her social networking account. Both the broadcast user and the subscriber can control respectively the distribution and reception of content with subscription settings. For examples, the settings may set limitations with respect to topics, contexts, and subscriber profile data.
US 2017/103343 published on Apr. 13, 2017 to Google Inc. discloses mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.
CN 103559262 that has a notification of grant of patent rights dated Jul. 29, 2016 to Beijing University pf Posts and Communications discloses a community-based author and academic paper recommending system and a recommending method. A double-layer quotation network consisting of an author layer and an academic paper layer is formed by utilizing a quotation relation between an author and the academic paper and the community information, then a user interesting model is established according to a historic behavior record of the user and the academic paper set read by the user, finally the user demand is analyzed according to the obtained double-layer quotation network and the user interesting model, and the author and academic paper thereof can be recommended to the user. The system is provided with an academic paper capturing module, an academic paper preprocessing module, a double-layer quotation network establishing module, a user interesting model establishing module and an individualized academic paper recommending module as well as a database. By adopting the recommending system and recommending method, not only can the correlation of the study content among users be used for establishing an author community through a subjective model, but also multiple attribute values of the to-be-recommended author and academic paper inside the community can be calculated, and the weakness that the calculation of the existing recommending algorithm is large can be improved; and meanwhile, multiple attribute values of the author and academic paper can be simultaneously calculated, so that the recommend result is more diversified, and the user requirement can be better met.